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GH200 vs H100

Explore a head to head comparison of specifications, performance, and pricing.

GH200

The NVIDIA GH200 is an advanced Hopper-based GPU that significantly boosts performance for generative AI, LLM, and HPC workloads with enhanced memory and bandwidth.

ManufacturerNVIDIA
GPU ArchitectureHopper
Average Price$2.86/hr
GPU VRAM96 GB
Cloud Availability2 clouds
System Memory480 GB
CPU Cores144
Storage4.8 TB

H100

The NVIDIA H100 is a Hopper-based GPU that provides exceptional performance, scalability, and economics for AI, deep learning, and HPC workloads.

ManufacturerNVIDIA
GPU ArchitectureHopper
Average Price$10.12/hr
GPU VRAM80 GB
Cloud Availability13 clouds
System Memory1920 GB
CPU Cores252
Storage31.3 TB

GH200 vs H100: Which Should You Choose?

The GH200 offers 96 GB of VRAM — 1.2× the 80 GB on the H100 — making it better suited for large model workloads that require holding more parameters in GPU memory. Memory bandwidth favors the GH200 at 0.00 TB/s compared to 0.00 TB/s on the H100, which directly impacts inference latency for memory-bandwidth-bound models. On Shadeform, the GH200 starts from $1.49/hr versus $1.66/hr for the H100 — 11% more expensive — reflecting the performance premium. The H100 is available across 13 cloud providers on Shadeform compared to 2 for the GH200, giving more options for region and pricing flexibility.

GH200 — Best Use Cases

  • Training large language models (7B–405B parameters)
  • High-throughput LLM inference
  • Mixture-of-experts and transformer workloads
  • Distributed multi-GPU training runs

Choose GH200 when:

  • You need 96 GB+ VRAM for large models or long context windows
  • Cost efficiency is your primary concern
  • Your preferred provider already has availability

H100 — Best Use Cases

  • Training large language models (7B–405B parameters)
  • High-throughput LLM inference
  • Mixture-of-experts and transformer workloads
  • Distributed multi-GPU training runs

Choose H100 when:

  • 80 GB VRAM is sufficient for your workload
  • Maximum performance justifies the higher cost
  • You need flexibility across multiple cloud providers or regions

See how the GH200 & H100 compare

Compare detailed hardware specifications and average pricing for the GH200 and H100.

Compare Hardware Specifications

GH200H100
GPU Type
GH200
H100
VRAM per GPU
96 GB
80 GB
Manufacturer
NVIDIA
NVIDIA
Architecture
Hopper
Hopper
Interconnect
NVLink-C2C
PCIe Gen5 or SXM5
Memory Bandwidth
4 TB/s or 4.9 TB/s
3.35 TB/s
FP16 TFLOPS
267.6 TFLOPS (4:1)
267.6 TFLOPS (4:1)
CUDA Cores
16896
16896
Tensor Cores
528 (4th Gen)
528 (4th Gen)
Base Clock
1500 MHz
1365 MHz
Boost Clock
1980 MHz
1785 MHz
TDP
900W-1000W
350-700W
Process Node
TSMC 4N
TSMC 4N
Data Formats
FP8, INT8, BF16, FP16, TF32, FP32, FP64
FP8, INT8, BF16, FP16, TF32, FP32, FP64

Compare Average On-Demand Pricing

GH200H100
1 GPU
$2.86 /hr
$2.85 /hr
2 GPUs
N/A
$5.19 /hr
4 GPUs
N/A
$9.79 /hr
8 GPUs
N/A
$19.35 /hr

Frequently Asked Questions: GH200 vs H100

The main differences are VRAM (96 GB vs 80 GB).

The GH200 is generally better for large language model training due to its higher throughput and 96 GB of VRAM, which allows fitting larger models or larger batch sizes in a single pass. For smaller models or fine-tuning tasks where cost matters more, both GPUs can be effective.

On Shadeform, the GH200 is available from $1.49/hr. The H100 starts from $1.66/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.

The GH200 has more VRAM at 96 GB, compared to 80 GB on the H100. Higher VRAM allows you to run larger models without quantization, use longer context windows, and process larger batch sizes — all of which improve throughput and reduce latency for memory-bound workloads.

Based on TFLOPS per dollar, the GH200 offers better raw compute value at current Shadeform on-demand rates. However, the best choice depends on your specific workload — if you need the extra VRAM or throughput of the H100, paying the premium may be justified by faster job completion and lower total cost.

The H100 is currently available across 13 cloud providers on Shadeform's network, compared to 2 for the GH200. Shadeform lets you deploy either GPU across all available providers from a single platform, so you can always find available capacity without manually checking each cloud.

Mixing different GPU types in a single training cluster is generally not recommended, as it creates performance bottlenecks where faster GPUs wait for slower ones. For best results, use a homogeneous cluster of either GH200 or H100. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.

Explore GH200 & H100 Instances

Browse available instances with GH200 and H100 GPUs. Filter by provider, availability, and more to find the perfect instance for your needs.

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